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IEEE TRANSACTIONS OF SOFTWARE ENGINEERING, VOL. 1, NO. 1, JUNE 2020 1

30 Years of Software Refactoring Research:A Systematic Literature Review

Chaima Abid, Vahid Alizadeh,Marouane Kessentini, Thiago do Nascimento Ferreira and Danny Dig

Abstract—Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools andtechniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improvethe system design while preserving the behavior. Refactoring studies are expanded beyond code-level restructuring to be applied atdifferent levels (architecture, model, requirements, etc.), adopted in many domains beyond the object-oriented paradigm (cloudcomputing, mobile, web, etc.), used in industrial settings and considered objectives beyond improving the design to include othernon-functional requirements (e.g., improve performance, security, etc.). Thus, challenges to be addressed by refactoring work are,nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring,recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of appliedrefactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, andobjectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes theresults of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literaturereview of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identifiedresearch trends, and highlighted gaps in the literature and avenues for further research.

Index Terms—Refactoring, systematic literature review, program transformation, software quality.

F

1 INTRODUCTION

For decades, code refactoring has been applied in infor-mal ways before it was introduced and properly defined inacademic work. The first known use of the term Refactoringin the published literature was in an article written byWilliam Opdyke and Ralph Johnson in September 1990[1]. William Griswold’s Ph.D. dissertation [2], publishedin 1991, is also one of the first major academic workson refactoring functional and procedural programs. Theauthor defined a set of automatable transformations anddescribed their impact on the code structure. One year later,William Opdyke also published his Ph.D. dissertation [3]on the Refactoring of object-oriented programs. In 1999,Martin Fowler published the first book about refactoringthat has as title Improving the Design of Existing Code [4].This book popularised the practice of code refactoring, setits fundamentals, and had a high impact on the world ofsoftware development. Martin Fowler defined Refactoringin his book as a sequence of small changes - called refac-toring operations - made to the internal structure of thecode without altering its external behavior. The goal of theserefactoring operations is to improve the code readability andreusability as well as reduce its complexity and maintenancecosts in the long run. Since then, a lot has changed in thesoftware development world, but one thing has remainedthe same: The need for Refactoring.

• Chaima Abid, Vahid Alizadeh, Marouane Kessentini, and Thiago doNascimento Ferreira are with the department of Computer and Informa-tion Science, University of Michigan, Dearborn, MI, USA.Danny Dig is with the Computer Science department, University ofColorado, Boulder, CO, USA.E-mail: [email protected]

Manuscript received on June 2020.

Nearly 30 years later, Refactoring has become a crucialpart of software development practice, especially with theever-changing landscape of IT and user requirements. It is acore element of agile methodologies, and most professionalIDEs include refactoring tools. Recent studies show thatrestructuring software systems may reduce developers’ timeby over 60% [5]. Others demonstrate how Refactoring canhelp detect, fix, and reduce software bugs [6]. Companiesare becoming more and more aware of the importance ofRefactoring, and they encourage their developers to contin-uously refactor their code to set a clean foundation for futureupdates.

It might be difficult for a developer to be justified tospend time on improving a piece of code to have the samefunctionality. However, it can be seen as an investmentfor future developments. Specifically, Refactoring is acrucial task on software with longer lifespans with multipledevelopers need to read and understand the codes.Refactoring can improve both the quality of software andthe productivity of its developers. Increasing the quality ofthe software is due to decreasing its complexity at designand source code level caused by refactoring, which is provedby many studies [7], [8]. The long-term effect of Refactoringis improving developers’ productivity by increasing twocrucial factors, understandability and maintainability of thecodes, especially when a new developer joins an existingproject. It is shown that Refactoring can help to detect, fix,and reduce software bugs and leading to software projectswhich are less likely to expose bug in development process[6]. Another study claims that there are some specific kindsof refactoring methods that are very probable to induce bugfixes [9].


1.1 Problem Description and Motivation

Refactoring is among the fastest-growing software engineer-ing research areas, if not the fastest. Figure 1 shows thedistribution of publications related to refactoring across theglobe. Figure 2 reflects the number of publications in thetop 10 most active countries in the field of Refactoring. TheUnited States tops the list of countries with a total of 714publications followed by Germany and Canada with a totalof 317 and 248 publications, respectively. During the past4 years, the number of published refactoring studies hasincreased with an average of 37% in all top 10 countries.This demonstrates a noticeable increase in interest/need inRefactoring.

Over 5584 authors from all over the world contributed tothe field of Refactoring. We highlight the most active authorsin Figure 3 and 4, based on both the number of publicationsand citations in the area. Many scholars started research inthe refactoring filed prior to 2000. Others are relatively newto the field and started their contributions after year 2010.All top 10 authors in the field have a constantly increasingnumber of publications over the past 20 years. MarouaneKessentini heads the list with a total of 43 publications(51% of them were published during the past five years)followed by Steve Counsell and Danny Dig with a total of39 and 36 publications, respectively. Marouane kessentinipublished an average of more than 4 articles per year whileall other authors published an average between 1.5 and2.75 publications per year. Figure 5 is a histogram showinghow many publications were issued each year starting from1990. The number of published journal articles, conferencepapers, and books has increased dramatically during the lastdecade, reaching a pick of 265 publications in 2016. Duringjust the last four years (2016-2019), over 1026 papers werepublished in the field, with an average of 256 papers eachyear.

Recently, several researchers and practitioners haveadopted the use of refactoring operations at higher degreesof abstraction than source code level (e.g., databases, Uni-fied Modeling Language (UML) models, Object ConstraintLanguage (OCL) rules, etc.). As a result, they often hadto redefine the principles and guidelines of refactoringaccording to the requirements and specifications of theirdomains. For instance, in User Interface Refactoring, de-velopers make changes to the UI to retain its semanticsand consistency for all users. These refactorings include,but not limited to, Align entry field, Apply common buttonsize, Apply font, Indicate format, and Increase color contrast.In Database Refactoring, developers improve the databaseschema by applying changes such as Rename column, Splittable, Move method, Replace LOB with table, and Introducecolumn constraint. Henceforth, the refactoring operations arecalled restructuring operations when applied to artifactsother than the ones related to object-oriented programming.Although the different refactoring communities (e.g., soft-ware maintenance and evolution, model-driven engineer-ing, formal methods, search-based software engineering,etc.) are interdependent in many ways, they remain dis-connected, which may create inconsistencies. For example,when model-level Refactoring does not match the code-level practice, it can lead to incoherence and technical issues

during development. The detachment is visible not onlybetween different refactoring domains but also betweenpractitioners and researchers. The distance between themprimarily originates from the lack of insights into bothworlds’ recent findings and needs. For instance, developerstend to use the refactoring features provided by IDEs dueto their accessibility and popularity. Most of the time, theyare uninformed of the benefits that can be derived fromadopting state-of-the-art advances in academia. All thesechallenges call for a need to identify, critically appraise, andsummarize the existing work published across the differentdomains. Existing systematic literature reviews examinefindings in very specific refactoring areas such as identifyingthe impact of refactoring on quality metrics [10] or codesmells [11]. To the best of our knowledge, no work collectsand synthesizes existing research, tools, and recent advancesmade in the refactoring community. This paper is the mostcomprehensive synthesis of theories and principles of refac-toring intended to help researchers and practitioners makequick advances and avoid reinventing or re-implementingresearch infrastructure from scratch, wasting time and re-sources. We also build a refactoring infrastructure that willconnect researchers with practitioners in industry and pro-vide a bridge between different refactoring communities inorder to advance the field of refactoring research.

1.2 Contributions

The Refactoring area is growing very rapidly, and manyadvances, challenges, and trends have lately emerged. Theprimary purpose of this study is to implement a systematicliterature review (SLR) for the field of refactoring as a whole.This SLR follows a defined protocol to increase the study’svalidity and rationality so that the output can be highin quality and evidence-based. We used various electronicdatabases and a large number of articles to comprise allthe possible candidate studies and cover more works thanexisting SLRs.

This SLR contributes to the existing literature in thefollowing ways:

• We identify a set of 3183 studies related to refactor-ing published until May 2020, fulfilling the qualityassessment criteria. These studies can be used bythe research and industry communities as a reliablebasis and help them conduct further research onRefactoring.

• We present a comprehensive qualitative and quanti-tative synthesis reflecting the state-of-the-art in refac-toring with data extracted from those 3183 high-rigorstudies. Our synthesis covers the following themes:artifacts, refactoring tools, different approaches, andperformance evaluation in refactoring research.

• We provide guidelines and recommendations basedon our findings to support further research in thearea.

• We implement a platform that includes the followingcomponents: (1) A searchable repository of refactor-ing publications based on our proposed taxonomy;(2) A searchable repository of authors who con-tributed to the refactoring community; (3) Analysis


Fig. 1. Distribution of refactoring publications around the world.

Fig. 2. Number of publications in the top 10 most active countries in the refactoring field

and visualization of the refactoring trends and tech-niques based on the collected papers. The proposedinfrastructure will allow researchers and practition-ers to easily report refactoring publications and up-load information about active authors in the field ofRefactoring. It will also bridge the different commu-nities to advance the field of refactoring research andprovide opportunities to educate the next refactoringgeneration.

1.3 Related Surveys

Mens et al. [12] provided an overview of existing researchin the field of software refactoring. They compared anddiscussed different approaches based on different criteriasuch as refactoring activities, techniques and formalisms,types of software artifacts that are being refactored, andthe effect of refactoring on the software process. Elish et al.[13] proposed a classification of refactoring methods basedon their measurable effect on software quality attributes.The investigated software quality attributes are adaptability,completeness, maintainability, understandability, reusabil-


Fig. 3. Top 10 Authors with the highest number of publications and citations in the field of refactoring

ity, and testability. Du Bois et al. [14] provided an overviewof the field of software restructuring and Refactoring. Theysummarized Refactoring’s current applications and toolsupport and discussed the techniques used to implementrefactorings, refactoring scalability, dependencies betweenrefactorings, and application of refactorings at higher levelsof abstraction. Mens et al. [15] identified emerging trends inrefactoring research (e.g., refactoring activities, techniques,tools, processes, etc.), and enumerates a list of open ques-tions, from a practical and theoretical point of views. Misb-hauddin et al. [16] provide a systematic overview of existingresearch in the field of model Refactoring. Al Dallal et al.[17] presented a systematic literature review of existingstudies, published through the end of 2013, identifying op-portunities for code refactoring activities. In another of theirwork [10], they presented a systematic literature review thatsummarizes the impact of refactoring on several internaland external quality attributes. Singh et al. [11] published asystematic literature review of refactoring concerning codesmells. However, the review of Refactoring is done in a gen-eral manner, and the identification of code smells and anti-patterns is performed in-depth. Abebe et al. [18] conducteda study to reveal the trends, opportunities, and challengesof software refactor researches using a systematic literaturereview. Baqais et al. [19] performed a systematic literaturereview of papers that suggest, propose, or implement anautomated refactoring process.

The different studies mentioned above are mainly aboutidentifying the studies related to very specific or specializedtopics. In this paper, we are trying to be as comprehensiveas possible by collecting, categorizing, and summarizing allthe papers related to refactoring in general that conform toour quality standards.

1.4 OrganizationThe rest of the paper is organized as follows: First, Section 2outlines the research method and the underlying protocolfor the systematic literature review. Section 3 describesthe proposed refactoring infrastructure. The results of thissystematic review are reported in Sections 4. Finally, Section5 presents the conclusions.

2 RESEARCH METHODOLOGY

Our literature review follows the guidelines established byKitchenham and Charters [20], which decompose a sys-tematic literature review in software engineering into threestages: planning, conducting, and reporting the review. Wehave also taken inspiration from recent systematic litera-ture reviews in the fields of empirical software engineering[10] and search-based software engineering [21]. All thesteps of our research are well documented, and all therelated data are available online for further validation andexploration []. This section details the performed researchsteps and the protocol of the literature review. First, section2.1 describes the research questions underlying our survey.Second, section 2.2 details the literature search step. Next,section 2.3 highlights the inclusion and exclusion criteria.The data preprocessing step and our proposed taxonomyare described in sections 2.4 and 2.5, respectively. The qual-ity assessment criteria are defined in section 2.6. Finally,Section 2.7 discusses threats to the validity of our study.

2.1 Research QuestionsThe following research questions have been derived basedon the objectives described in the introduction, which formthe basis for the literature review:


Fig. 4. Evolution of the Top 10 Authors during the past 10 years

Fig. 5. Trend of publications in the field of refactoring during the lastthree decades.

• RQ1: What is the refactoring life-cycle?• RQ2: What are the types of artifacts that are being

refactored at each step of the refactoring life-cycle?• RQ3: Why do software practitioners and researchers

perform refactoring?• RQ4: What are the different approaches used by

software practitioners and researchers to performrefactoring?

• RQ5: What types of datasets are used by softwarepractitioners and researchers to validate the refactor-ing?

2.2 Literature Search StrategyAll the papers have been queried from a wide range of scien-tific literature sources to make our search as comprehensiveas possible:

• Digital libraries: ACM Library, IEEE Xplore, Science-Direct, SpringerLink.

• Citation databases: Web of Science (formerly ISI Webof Knowledge), Scopus.

• Citation search engines: DBLP, Google Scholar.

We first defined a list of terms covering the variety of bothapplication domains and refactoring techniques. For that,we checked the title, keywords, and abstract of the relevantpapers that were already known to us. Synonyms andkeywords were derived from this list. These keywords werecombined using logical operators ANDs and ORs to createsearch terms. Before starting collecting the primary studies(PS), we tested the search terms’ effectiveness on all thedata sources. Then, we refined the queries to avoid gettingirrelevant papers. The string adjustments were agreed onby all authors. The final list of search strings are shownin Table 1. These search strings were modified to suit thespecific requirements of different electronic databases. Weconducted our search on May 31st, 2020, and identifiedstudies published up until that date. The search was donefirst by the corresponding author and then verified by therest of the authors. In our systematic review, we followed amulti-stage model to minimize the probability of missingrelevant publications as much as possible. The differentstages are shown in figure 6 along with the total returnedpublications at each stage. The first stage consists of execut-ing the search queries on the databases mentioned above;a total of 6158 references were found. Then, we removedthe duplicates, which reduced the list of candidate papersto 3882. Then, we performed a manual examination oftitles and abstracts to discard irrelevant publications basedon the inclusion and exclusion criteria. We also looked atthe body of the paper whenever necessary. This decreasedthe list of candidate papers to 3161 publications. Next, we


Select Scientific Literature Sources

Define Search Strings

Executing the Search Queries(6158 publications)Stage 1

Check Cross-references of Relevant Publications(3178 publications)Stage 5

Remove Duplicates(3882 publications)Stage 2

Manual Examination of Titles and Abstracts(3161 publications)Stage 3

Consult Web Profiles of Relevant Authors(3164 publications)Stage 4

Contact Authors(3183 publications)Stage 6

-2276

-721

+3

+14

+5

Fig. 6. SLR steps

used the resulting set as input for the snowballing process,recommended by Wohlin [22], to identify additional studies.We consulted web profiles of relevant authors and theirnetworks. We also checked cross-references until no furtherpapers were detected. As a result, 17 new references wereadded. After that, we contacted the corresponding authorsof the identified publications to inquire about any missingrelevant studies. This led to adding 5 studies.

2.3 Inclusion and Exclusion Criteria

To filter out the irrelevant articles among those selected inStage 2 and determine the Primary studies, we consideredthe following inclusion and exclusion criteria.

2.3.1 Inclusion criteriaAll of the following criteria must be satisfied in the selectedprimary studies:

1) The article must have been published in a peerreviewed journal or conference proceeding betweenthe years 1990 and 2020. The main reason for im-posing a constraint over the start year is becausethe first known use of the term “refactoring” inthe published literature was in a September, 1990article by William Opdyke and Ralph Johnson [1].We included papers up till May 31st 2020.

2) The article must be related to computer science andengineering and propose techniques, methods andtools for refactoring.

3) The paper must be written in English.

4) In case a conference paper has a journal extension,we would include both the conference and journalpublications.

5) The paper must pass the quality assessment criteriathat are elaborated in Section 2.6.

2.3.2 Exclusion criteriaPapers satisfying any of the exclusion criteria were dis-carded, as follows:

1) Studies that are not related to the computer sciencefield.

2) Studies that investigated the impact of generalmaintenance on code quality. In this case, the main-tenance tasks were potentially performed due toseveral reasons and not limited to refactoring, andtherefore, we cannot judge whether the impact wasdue to refactoring or to other maintenance taskssuch as corrective or adaptive maintenance.

3) Grey Literature

2.4 Data PreprocessingA pre-processing technique was applied to improve reliabil-ity and precision, as detailed in the following sub sections.

2.4.1 Simplifying Author’s nameIn general, scientific and bibliographic databases such asWeb of Science (WoS) and Scopus have the following incon-sistencies in authors names:

• Most journals abbreviate the author’s first name toan initial and a dot.

• Most journals use the author name’s special accents.• WoS uses a comma between the author’s last name

and first name initial, but Scopus does not.

These name-related inconsistencies mean that sciento-metrics scripts cannot find all of the similar author’s names.For that reason, ScientoPy script applies the following stepsto simplify author’s name fields:

• Remove dots and coma from author’s name.• Remove special accents from author’s name

2.4.2 Fixing inconsistent country namesSome authors use different naming to refer to the samecountry (such as USA and United States). For that reason,some country names were replaced based on Table 3.

2.5 Study ClassificationAccording to the research questions listed in Section 2.1, weclassified the PSs into five dimensions: (1) refactoring life-cycle (related to RQ1), (2) artifacts affected by refactoring(related to RQ2), (3) refactoring objectives (related to RQ3),(4) refactoring techniques (related to RQ4) and (5) refactor-ing evaluation (related to RQ5). The determination of theattributes of each dimension was performed incrementally.That is, for each dimension, we started with an emptyset of attributes. The authors of this study screened thefull texts of the PSs one by one, analyzed each reportedstudy based on the considered dimension, and determined


TABLE 1final list of search strings

search strings(software OR system OR code OR service OR diagram OR database OR architecture OR Model ORGUI OR user interface OR UI OR design OR artifact OR developer OR computer OR programmingOR object-oriented OR implement OR mobile app OR cloud OR document ) AND (refactor ORrefactoring)

TABLE 2PS quality assessment questions [17]

Question

Design

Are the applied identification techniques for refactoring opportunities clearly described?Are the refactoring activities considered clearly stated and defined?Was the sample size justified?Are the evaluation measures fully defined?

Conduct Are the data collection methods adequately described?

Analysis

Are the results of applying the identification techniques evaluated?Are the data sets adequately described? (size, programming languages, source)Are the study participants or observational units adequately described?Are the statistical methods described?Are the statistical methods justified?Is the purpose of the analysis clear?Are the scoring systems (performance evaluation) described?

Conclusion

Are all study questions answered?Are negative findings presented?Are the results compared with previous reports?Do the results add to the literature?Are validity threats discussed?

TABLE 3List of countries and their replacements

Country ReplacementRepublic of China ChinaUSA United StatesEngland, Scotland and Wales EnglandU Arab Emirates United Arab EmiratesRussia Russian FederationViet Nam VietnamTrinid & Tobago Trinidad and Tobago

the attributes of that dimension as considered by each PS.Table 4 outlines the keywords extracted for each category.It should be pointed out that, most of the time, we removeall of the affixes (i.e., suffixes, prefixes, etc.) attached to aword in order to keep its lexical base, also known as rootor stem or its dictionary form or lemma. For instance, theword document allows us to detect the words documentationand documenting. Also, we did not include bi-grams andtri-grams that can be detected using one uni-gram. Forexample, Class Diagram, Object Diagram, Sequence Diagram,and Use Case Diagram can all be detected using the wordDiagram alone.

The screening of the PSs resulted in determining sixstages for the refactoring life-cycle (e.g., detection, pri-oritization, recommendation, testing, documentation, andprediction). We also classified the papers according to thelevel of automation of the proposed technique (e.g., auto-matic, manual, semi-automatic). The results are describedin section 4.1. For the second dimension, we identified fiveartifacts on which the impact of refactoring is studied byat least one of the PSs. These artifacts are code, architec-ture, model, GUI, and database. The classification of PSsbased on these artifacts is discussed in detail in Section4.2. We subdivided the third dimension into five categories

(e.g., External quality, internal quality, performance, migra-tion, and security) to reflect the refactoring objective andsix categories (e.g., Object-oriented design, Aspect-orienteddesign, Model-driven engineering, Documentation, Mobiledevelopment, and Cloud computing) to describe the refac-toring paradigms. The classification of PSs based on thesecategories is discussed in detail in Section 4.3. We dividedthe fourth dimension into four categories (e.g., data mining,search-based algorithms, formal methods, and fuzzy logic)to reveal the refactoring techniques adopted in the studiesand into twelve categories (e.g., Java, C, C#, Python, Cobol,PHP, Scala, Smalltalk, Ruby, Javascript, MATLAB, and CSS)to show the most common programming languages used inour PSs. The details of this categorization are reported insection 4.4. Finally, for the fifth dimension, we divide thePSs into two categories: open-source and industrial. Theopen-source category includes studies that validate theirapproaches using open source systems. In contrast, theindustrial category consists of the studies that validate theirwork on systems of their industrial collaborators. Thesefindings are outlined in Section 4.5.

2.6 Study Quality Assessment

To ensure a level of quality of papers, we only includedvenues that are known for publishing high-quality softwareengineering research in general with an h-index of at least10, as has been done by [23] . Each of the papers thatwere published before 2019 has to be cited at least once.The quality of each primary study was assessed based ona quality checklist defined by Kitchenham and Charters[20]. This step aims to extract the primary studies withinformation suitable for analysis and answering the definedresearch questions. The quality checklist, (described in table2) were defined by Galster et al. [23]. They are developed


TABLE 4List of keywords used to detect the different categories

Category KeywordsRefactoring life-cycle (RQ1)Detection detect, opportunity, smell, antipattern, design defectPrioritization schedul, sequence, prioritRecommendation recommend, correction, correcting, fixing, suggestTesting test, regression testing, test case, unit testDocumentation documentPrediction predict, future release, next release, development history, refactoring historyLevel of automation (RQ1)Manual manualSemi-automatic semi-automat, semi-manualAutomatic automatArtifact (RQ2)Code code, java, object orient, smell, antipattern, anti-pattern, object-orientModel design, model, UML, diagram, Unified Modeling LanguageArchitecture architecture, hotspot, hierarchyGUI gui, user interface, UIDatabase relational, schema, database, Structured Query Language, SQLParadigm (RQ3)Object-oriented design object orient, object-orient, oo, java, c, ++, python, C sharp, c#, css, Python, R, PHP, JavaScript, Ruby,

Perl, Object Pascal, Objective-C, Dart, Swift, Scala, Kotlin, Common Lisp, MATLAB, SmalltalkAspect-oriented design aspectModel-driven engineering model transform, uml, reverse engineering, diagram, Unified Modeling LanguageDocumentation documentMobile development android, mobile, IOS, phone, smartphone, cellphonesCould computing web service, wsdl, restful, cloud, Apache Hadoop, Docker, Middleware, Software-as-a-Service,

SaaS, XaaS, Anything-as-a-Service, Platform-as-a-Service, PaaS, Infrastructure-as-a-Service, IaaS, AWS,Amazon EC2, Amazon Simple Storage Service, S3

Refactoring Objectives (RQ3)Internal Quality maintainability, cyclomatic, depth of inheritance, coupling, quality, Flexibility, Portability, Re-usability,

Readability, Testability, UnderstandabilityPerformance performance, parallel, Response Time, Error Rates, Request Rate, availabilityExternal quality analysability, changeability, time behaviour, resource, Correctness, Usability, Efficiency, Reliability,

Integrity, Adaptability, Accuracy, RobustnessMigration migratSecurity secure, safety, Attack surface, virus, hack, vulnerability, vulnerable, spamProgramming languages (RQ4)Java javaC c, c++C# c sharp, c#Python pythonCSS cssPHP phpCobol cobolScala scalaJavascript javascriptRuby rubySmalltalk smalltalkMATLAB matlabAdopted methods (RQ4)Search-based algorithms search, search-base, sbse, genetic, fitness, simulated annealing, tabu search, search space, Hill climbing,

Multi-objective evolutionary algorithms, multi objective optimization, multi-objective programming,vector optimization, multi-criteria optimization, multi-attribute optimization, Pareto optimization,Evolutionary Multi-objective Optimization, EMO, Single-Objective Optimization, Many-ObjectiveOptimization, multi objective

Data mining artificial intelligence, ai , machine learning, naive bayes, decision tree, SVM, support vector machine,Cluster, Classification, classify, Association, Neural networks, deep learning, random forest, regression,reinforcement learning, learning

Formal methods model check, formal method, B-Method, RAISE, Z notation, SPARK AdaFuzzy logic fuzzyEvaluation method (RQ5)Open source open source, open-sourceIndustrial proprietary, industrial, industry, collaborator, collaboration


by considering bias and validity problems that can occurat different stages, including the study design, conduct,analysis, and conclusion. Each question is answered by a”Yes”, ”Partially”, or ”No”, which correspond to a score of1, 0.5, or 0, respectively. If a question does not apply to astudy, we do not evaluate the study for that question. Thequality assessment checklist was independently applied toall 3882 studies by two of the authors. All disagreementson the quality assessment results were discussed, and aconsensus was reached eventually. Few cases where agree-ment could not be reached were sent to the third author forfurther investigation. 154 studies did not meet the qualityassessment criteria.

2.7 Threats to ValiditySeveral limitations may affect the generalizability and theinterpretations of our results. The first is the possibility ofpaper selection bias. To ensure that the studies were se-lected in an unbiased manner, we followed the well-definedresearch protocol and guidelines reported by Kitchenhamand Charters [20] instead of proposing nonstandard qualityfactors. Also, the final decision on the articles with selectiondisagreements was performed based on consensus meet-ings. The Primary studies were assessed by one researcherand checked by the other, a technique applied in similarstudies [21]. The second threat consists of missing a rele-vant study. To overcome this threat, we employed severalstrategies that we mentioned in Section 2.2. Few relatedstudies were detected after performing the automatic search,which indicates that the constructed search strings and thementioned utilized libraries were comprehensive enough toidentify most of the relevant articles. Another critical issueis whether our taxonomy is complete and robust sufficientto analyze and classify the primary studies. To overcomethis problem, we used an iterative content analysis methodby going through the papers one by one and continuouslyexpand the taxonomy for every new encountered concept.Furthermore, to gather sufficient keywords to detect thedifferent categories, we followed the same iterative process,and we added synonyms based on the authors’ expertisein the field of refactoring. Another threat is related to thetagging of the papers according to our taxonomy. To miti-gate this problem, we asked 27 graduate students to checkthe correctness of the classification results by reading theabstract, the title, and keywords. They also check the bodyof the paper whenever necessary.

3 REFACTORING INFRASTRUCTURE

We implemented a large scale platform [24] that collects,manages, and analyzes refactoring related papers to helpresearchers and practitioners share, report, and discoverthe latest advancements in software refactoring research. Itincludes the following components:

1) A searchable repository of refactoring publicationsbased on our proposed taxonomy. Figure 9 shows ascreenshot of the publications’ tab of the refactoringrepository website. The papers can be searched byauthor, title, or year of publication. Each paper hastags that describe its content based on our taxonomy

described in section 2.5. The papers can also befiltered using those tags and sorted alphabeticallyor chronologically according to the title and yearof publication, respectively. The user can export thepublications’ dataset to many formats, includingpdf, excel, and CSV. He can also easily report a newpublication by entering its link.

2) A searchable repository of authors who con-tributed to the refactoring community. Figure8 shows a screenshot of the authors’ tab of therefactoring repository website. The authors can besearched and sorted alphabetically by name, affil-iation, or country. They can also be sorted basedon the total number of refactoring publications.The user can also consult the Google Scholar andScopus profiles of the authors if available. Finally,the user can easily report a new author by enteringtheir information and their profile. Furthermore, wedefined the refactoring h-index, which shows howmany papers about refactoring published by theauthor have been cited proportionately. A refac-toring h-index of X means that the author has Xpapers about refactoring that have been cited atleast X times. Authors can also be sorted accordingto the refactoring h-index and the total number ofcitations (see figure 11). Besides, we created a co-author network and corresponding visualizations(see figure 12) to get a snapshot view of the breadthand depth of an individual’s collaborations in thefield of refactoring research. Finally, we generated ahistogram (see figure 7) that shows the number ofpublications issued by the top institutions active inthe refactoring research by considering the authors’affiliations.

3) Analysis and visualization of the refactoringtrends and techniques based on the collected pa-pers. Figure 10 shows a screenshot of the refactoringrepository dashboard. It contains histograms andpie charts that show the distribution and percent-ages of the categories defined in our taxonomy. Italso includes maps that reflect the spread of refac-toring activity across the world.

The proposed infrastructure will enable researchers toperform a fair comparison between their new refactoringapproaches and state-of-the-art tools; enable researchers touse refactoring data of large software systems; facilitate in-teractions between researchers from currently disconnecteddomains/communities of refactoring (model-driven engi-neering, service computing, parallelism and performanceoptimization, software quality, testing, etc.); enable practi-tioners and researchers to quickly identify relevant existingresearch papers and tools for their problems based on theproposed taxonomy and classification; create benchmarksagainst which various refactoring approaches can be evalu-ated; enable effective interactions between practitioners andrefactoring researchers to identify relevant problems facedby the software industry.


Fig. 7. Top institutions active in the refactoring field

Fig. 8. A screenshot of the authors tab of the refactoring repositoryWebsite

4 RESULTS

In this section, we aim to answer the research questions. Toprovide an overview of the current state of the art in refac-toring and guide the reader to a specific set of approaches,tools, and recent advances that are of interest, we classifiedthe 3183 reviewed papers based on the taxonomy describedin Section 2.5. Table 5 contains representative references forthe categories created for each RQ. We only provided 10references per category because we cannot possibly reportin this paper the categorization of all the studies sincewe are dealing with a total of 3183 papers. The resultsof the classification of all the papers are provided in ourwebsite [24]. For some taxonomy categories, papers mayhave multiple values and thus be listed several times. Asa result, percentages in the tables may sum up to more than100 percent. Also, not all the papers were classified in alldimensions. Consequently, percentages in one dimensionmay not sum up to 100 percent. The rest of this sectionpresents the observations and insights that can be derivedfrom the visualization of the categories.

4.1 Refactoring life-cycleGoing through the primary studies, we have been able toestablish a refactoring life-cycle that is composed of six

Fig. 9. A screenshot of the publications tab of the refactoring repositoryWebsite

Fig. 10. A screenshot of the Dashboard of the refactoring repositorywebsite

stages:

• Refactoring detection: Identifying refactoring op-portunities is an important stage that precedes theactual refactoring process. It can be done by man-ually inspecting and analyzing an artifact of a sys-tem to identify refactoring opportunities. However,this technique is time-consuming and costly. Re-searchers in this area typically propose fully or semi-automated techniques to identify refactoring oppor-tunities. These techniques may be applicable to dif-ferent artifacts and should be evaluated empirically.

• Refactoring prioritization: The number of refactor-ing opportunities usually exceeds the amount ofproblems that the developer can deal with, par-ticularly when the effort available for performingrefactorings is limited. Moreover, not all refactoringopportunities are equally relevant to the goals of thesystem or its health. In this stage, the refactorings op-erations are prioritized using different criteria (e.g.,maximizing the refactoring of classes with a large


TABLE 5Representative references for all categories

Category Percentage PapersRefactoring life-cycle (RQ1)Detection 28.65% [S1], [S2], [S3], [S4], [S5], [S6], [S7], [S8], [S9], [S10]Prioritization 9.43% [S11], [S12], [S13], [S14], [S15], [S16], [S17], [S18], [S19], [S20]Recommendation 16.18% [S3], [S11], [S12], [S21], [S22], [S23], [S24], [S25], [S26], [S27]Testing 18.44% [S4], [S6], [S7], [S8], [S13], [S28], [S29], [S30], [S31], [S32]Documention 5.22% [S33], [S34], [S35], [S36], [S37], [S38], [S39], [S40], [S41], [S42], [S43]Prediction 4.818% [S44], [S45], [S46], [S47], [S48], [S49], [S50], [S51], [S52], [S53]Level of automation (RQ1)Automatic 30.95% [S54], [S55], [S56], [S57], [S58], [S59], [S60], [S61], [S62], [S63]Semi-automatic 1.95% [S64], [S65], [S66], [S67], [S68], [S69], [S70], [S71], [S72], [S73], [S74], [S75]Manual 8.67% [S69], [S76], [S77], [S78], [S79], [S80], [S81], [S82], [S83], [S84]Artifact (RQ2)Code 72.89% [S1], [S2], [S3], [S11], [S65], [S85], [S86], [S87], [S88], [S89]Model 59.25% [S1], [S3], [S28], [S29], [S65], [S87], [S89], [S90], [S91], [S92]Architecture 17.25% [S28], [S91], [S93], [S94], [S95], [S96], [S97], [S98], [S99], [S100]GUI 2.58% [S6], [S8], [S28], [S87], [S89], [S90], [S101], [S102], [S103], [S104]Database 4.12% [S27], [S36], [S65], [S100], [S105], [S106], [S107], [S108], [S109], [S110]Paradigm (RQ3)Object-oriented design 34.09% [S1], [S8], [S30], [S85], [S87], [S88], [S101], [S111], [S112], [S113]Aspect-oriented 10.87% [S88], [S96], [S101], [S102], [S103], [S114], [S115], [S116], [S117], [S118]Model-driven engineering 7.35% [S3], [S15], [S32], [S58], [S65], [S119], [S120], [S121], [S122], [S123]Mobile apps development 3.55% [S23], [S87], [S87], [S95], [S99], [S112], [S124], [S125], [S126], [S127]Could computing 4.15% [S128], [S129], [S130], [S131], [S132], [S133], [S134], [S135], [S136], [S137]Refactoring Objective (RQ3)Internal Quality 41.63% [S3], [S12], [S21], [S29], [S30], [S89], [S90], [S94], [S138], [S139]Performance 15.93% [S10], [S12], [S28], [S86], [S88], [S91], [S92], [S96], [S115], [S119]External quality 22.68% [S87], [S91], [S92], [S95], [S102], [S140], [S141], [S142], [S143], [S144]Migration 3.61% [S95], [S100], [S113], [S145], [S146], [S147], [S148], [S149], [S150], [S151]Security 3.11% [S113], [S152], [S153], [S154], [S155], [S156], [S157], [S158], [S159], [S160]Programming language (RQ4)Java 17.15% [S1], [S8], [S10], [S30], [S85], [S87], [S88], [S112], [S113], [S140]C 4.65% [S59], [S96], [S104], [S105], [S111], [S146], [S161], [S162], [S163], [S164]C# 0.66% [S61], [S165], [S166], [S167], [S168], [S169], [S170], [S171], [S172], [S173]Python 0.53% [S174], [S175], [S176], [S177], [S178], [S179], [S180], [S181], [S182], [S183]CSS 0.5% [S147], [S184], [S185], [S186], [S187], [S188], [S189], [S190], [S191], [S192]PHP 0.35% [S169], [S193], [S194], [S195], [S196], [S197], [S198], [S199], [S200], [S201]Cobol 0.31% [12], [S202], [S203], [S205], [S206], [S207], [S208], [S209]MATLAB 0.28% [S210], [S211], [S212], [S213], [S214], [S215], [S216], [S217]Smalltalk 0.79% [25], [S219], [S220], [S221], [S222], [S223], [S224], [S225], [S226], [S227]Ruby 0.22% [S169], [S181], [S228], [S229], [S230], [S231]Javascript 0.72% [S112], [S232], [S233], [S234], [S235], [S236], [S237], [S238], [S239], [S240], [S241]Scala 4.02% [S33], [S55], [S86], [S126], [S242], [S243], [S244], [S245], [S246], [S247]Adopted Method (RQ4)Search-based algorithms 25.76% [S12], [S248], [S249], [S250], [S251], [S252], [S253], [S254], [S255], [S256]Data mining 15.49% [S2], [S82], [S107], [S185], [S257], [S258], [S259], [S260], [S261], [S262]Formal methods 2.92% [S42], [S199], [S263], [S264], [S265], [S266], [S267], [S268], [S269]Fuzzy logic 0.28% [S257], [S270], [S271], [S272], [S273], [S273], [S274]Evaluation method (RQ5)Open source 16.31% [S1], [S7], [S12], [S30], [S32], [S88], [S112], [S139], [S248], [S275]Industrial 10.4% [S9], [S12], [S16], [S115], [S120], [S147], [S276], [S277], [S278], [S279]

Fig. 11. A screenshot of the refactoring repository dashboard that showsthe authors, their h-index and total number of publications and citations

number of anti-patterns or with the previous history

of bugs, etc.) according to the needs of developers.• Refactoring recommendation: Several refactoring

recommendation tools have been proposed that dy-namically adapt and suggest refactorings to develop-ers. The output is sequences of refactorings that de-velopers can apply to improve the quality of systemsby fixing, for example, code smells or optimizingsecurity metrics.

• Refactoring testing: After choosing the refactoringsto be applied, tests need to be done to ensure the cor-rectness of artifacts transformations and avoid futurebugs. This is done by checking the satisfaction of thepre-and post-conditions of the refactoring operationsand the preservation of the system behavior.

• Refactoring documentation: After applying and test-ing the refactorings, we need to document the refac-


Fig. 12. A screenshot of the authors network graph from the refactoringrepository website

torings, their locations, why they have been applied,and the quality improvements.

• Prediction: It is interesting for developers to knowwhich locations are likely to demand refactoring infuture releases of their software products. This willhelp them focus on the relevant artifacts that willundergo changes in the future, prepare them for fur-ther improvements and extensions of functionality,and optimize the management of limited resourcesand time. Predicting locations of future refactoringcan be done using the development history.

Figure 13 illustrates the percentage of the papers relatedto each stage of the refactoring life-cycle. 33.08% of thepapers deal with testing. Researchers have invested heavilyin testing to ensure the reliability of refactoring becausechanging the structure of code can easily introduce bugsin the program and lead to challenging debugging sessions.A plenty of effort is made towards the automation of thetesting process to facilitate the adoption of refactoring [S54],[S55], [S56]. Detecting refactoring opportunities is also atopic of interest to researchers. Several approaches havebeen proposed to detect refactoring opportunities includingbut not limited to techniques that depend on quality metrics(e.g., cohesion, coupling, lines of code, etc.), code smells(e.g., feature envy, Blob class, etc.), Clustering (similaritiesbetween one method and other methods, distances betweenthe methods and attributes, etc.), Graphs (e.g., representthe dependencies among classes, relations between methodsand attributes, etc.), and Dynamic analysis (e.g., analyzingmethod traces, etc.). Refactoring documentation is an under-explored area of research. Only 5.22% of the collected pa-pers dived into refactoring documentation. Many studiesexamined the automation of the different refactoring stagesto reduce the refactoring effort and, therefore, increase itsadaption. Figure 14 shows the count of publications dealingwith manual, semi-automatic, and automated refactoring.In fact, 30.95% of the papers deal with the automationof refactoring. Only 1.95% and 8.67% of the papers usedmanual and semi-automatic refactoring, respectively.

4.2 Artifacts affected by refactoringAs we mentioned before, refactoring is not limited to soft-ware code. In fact, it can be applied to any type of soft-ware artifacts (e.g., software architectures, database schema,models, user interfaces, and code). Figure 15 shows the per-centage of refactoring publications per artifact. The evidencefrom this histogram shows that the most popular refactoringartifact is code (72.89%). Model refactoring has also receivedconsiderable attention, with a percentage of 59.25%. Graph-ical user interfaces (GUIs) and Database refactoring havereceived the least attention of all with a fraction of only4.12% and 2.58%, respectively. This might be due to the factthat database refactoring is conceptually more difficult thancode refactoring; code refactorings only need to maintainbehavioral semantics while database refactorings also mustmaintain informational semantics. Also, GUI refactoring isvery demanding, requiring the adoption of user interfacesarchitectural patterns from the early software design stages.Future research should explore database and user interfacerefactoring further as they are an indispensable part oftoday’s software.

4.3 Refactoring objectivesFive paradigms have been identified from analyzing theprimary studies: object-oriented designs, cloud computing,mobile apps, model-driven, and aspect-oriented. Object-oriented programming has gained popularity because itmatches the way people actually think in the real world,structuring their code into meaningful objects with relation-ships that are obvious and intuitive. The increased popular-ity of the object-oriented paradigm has also increased theinterest in object-oriented refactoring. This can be observedin figure 16 where more than 34% of the studies related torefactoring focus on object-oriented designs. Less than 5% ofthe papers investigated refactoring for cloud computing andmobile app development. For the refactoring objectives clas-sification of the taxonomy, five subcategories are considered:external quality (e.g. correctness, usability, efficiency, relia-bility, etc.) , internal quality (e. g. maintainability, flexibility,portability, re-usability, readability etc.) , performance (e.g.response time, error rate, request rate, memory use, etc.),migration (e.g. Dispersion in the Class Hierarchy, numberof referenced variables, number of assigned variables etc. ),security (e.g. time needed to resolve vulnerabilities, Numberof viruses and spams blocked, Number of port probes,number of patches applied, Cost per defect, Attack surfaceetc.). Figure 17 is illustrating the reasons why people refactortheir systems. Improving the internal quality takes up thelargest portion (41.63%) followed by refactoring to improvethe external quality (22.68%). Although security is a majorconcern for almost all systems, only 3.11% of the papersinvestigated refactorings for security reasons.

4.4 Refactoring techniquesObject-oriented programming languages have commontraits/properties that facilitate the development of widelyautomated source code analysis and transformation tools.Many studies [25] have given sufficient proof that a refac-toring tool can be built for almost any object-oriented lan-guage (Python, PHP, Java, and C++). Support for multiple


Fig. 13. Histogram illustrating the percentage of refactoring publications per refactoring life-cycle

Fig. 14. Histogram illustrating the percentage of publications dealing withmanual, semi-automatic and automated refactoring

languages in a refactoring tool is mentioned by [26]. Javais probably the most commercially important recent object-oriented language with an infrastructure that is designedto support analysis. It has generic parsing, tree building,prettyprinting, tree manipulation, source-to-source rewrit-ing, attribute grammar evaluations, control, and data flowanalysis. This explains the fact that 17.15% of refactoringstudies (see figure 18) provided refactoring techniques andtools that support Java. At the same time, most of the otherprogramming languages have a fraction of less than 1%.We classified the refactoring techniques into four main cat-egories: data mining (e.g., Clustering, Classification, Deci-sion trees, Association, Neural networks, etc.), search-basedmethods (e.g., Genetic algorithms, Hill climbing, Simulatedannealing, Multi-objective evolutionary algorithms, etc.),formal methods (B-Method, the specification languagesused in automated theorem proving, RAISE, the Z notation,

Fig. 15. histogram illustrating the count of refactoring publications perartifact

SPARK Ada, etc.), and fuzzy logic. More than 25% of thepapers use Search-based techniques to address refactoringproblems (see figure 19). This can be explained by thefact that search-based approaches have been proven to beefficient at finding solutions for complex and labor-intensivetasks. With the growing complexity of software systems,there’s an infinite amount of improvement/changes you canmake to any piece of artifact. Exact algorithms are hard touse to solve the refactoring problem within an instance-dependent, finite run-time. That’s why finding optimalrefactoring solutions are sacrificed for the sake of gettingperfect solutions in polynomial time using heuristic meth-ods like search-based algorithms. Data mining techniqueshave also received significant attention (17.59%) as they areknown to be efficient at discovering new information, suchas unknown patterns or hidden relationships, from hugedatabases like, for our case, large code repositories.

4.5 Refactoring evaluationOpen-source software systems are becoming increasinglyimportant these days. 61.1% of the studies (see figure 20)


Fig. 16. Histogram illustrating the count of refactoring publications per paradigm

Fig. 17. Histogram illustrating the count of publications per refactoring objective

used open-source systems to validate their work comparedto 38.9% of studies that validated their work on industrialprojects. This result is expected because of the availabilityand accessibility of open source systems. However, open-source software is often developed with a different man-agement style than the industrial ones. Thus, refactoringtechniques and tools must be validated and checked forquality and reliability using industrial systems. More indus-trial collaborations are needed to bridge the gap betweenacademic research and the industry’s research needs, andtherefore, produce groundbreaking research and innovationthat solves complex real-world problems.

5 CONCLUSION

In this paper, we have conducted a systematic literaturereview on refactoring accompanied by meta-analysis to an-swer the defined research questions. After a comprehensive

search that follows a systematic series of steps and assessingthe quality of the studies, 3183 publications were identified.Based on these selected papers, we derived a taxonomyfocused on five key aspects of Refactoring: refactoring life-cycle, artifacts affected by refactoring, refactoring objectives,refactoring techniques, and refactoring evaluation. Usingthis classification scheme, we analyzed the primary studiesand presented the results in a way that enables researchersto relate their work to the current body of knowledge andidentify future research directions. We also implemented arepository that helps researchers/practitioners collect andreport papers about Refactoring. It also provides visualiza-tion charts and graphs that highlight the analysis results ofour selected studies. This infrastructure will bridge the gapamong the different refactoring communities and allow formore effortless knowledge transfer. To conclude, we believethat the results of our systematic review will help advancethe refactoring research area. Since we expect this research


Fig. 18. histogram illustrating the count of refactoring publications per programming language

Fig. 19. histogram illustrating the count of refactoring publications perfield

area to continue to grow in the future, we hope that ourrepository and taxonomy will become useful in organizing,developing and judging new approaches.

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Chaima Abid is currently a PhD student in theintelligent Software Engineering group at theUniversity of Michigan. Her PhD project is con-cerned with the application of intelligent searchand machine learning in different areas such asweb services, refactoring and security. Her cur-rent research interests are Search-Based Soft-ware Engineering, web services, refactoring, se-curity, data analytics and software quality.

Vahid Alizadeh is currently a Ph.D. student inthe intelligent Software Engineering group at theUniversity of Michigan. His Ph.D. project is con-cerned with the application of intelligent searchand machine learning in different software engi-neering areas such as refactoring, testing, anddocumentation. His current research interestsare Search-Based Software Engineering, Refac-toring, Artificial Intelligence, data analytics andsoftware quality.

Marouane Kessentini is a recipient of the pres-tigious 2018 President of Tunisia distinguishedresearch award, the University distinguishedteaching award, the University distinguished dig-ital education award, the College of Engineeringand Computer Science distinguished researchaward, 4 best paper awards, and his AI-basedsoftware refactoring invention, licensed and de-ployed by industrial partners, is selected as oneof the Top 8 inventions at the University of Michi-gan for 2018 (including the three campuses),

among over 500 inventions, by the UM Technology Transfer Office. Heis currently a tenured associate professor and leading a research groupon Software Engineering Intelligence. Prior to joining UM in 2013, Hereceived his Ph.D. from the University of Montreal in Canada in 2012.He received several grants from both industry and federal agencies andpublished over 110 papers in top journals and conferences. He hasseveral collaborations with industry on the use of computational search,machine learning and evolutionary algorithms to address software engi-neering and services computing problems.

Thiago do Nascimento Ferreira is a Post-doctoral Researcher at University of Michigan-Dearborn under the supervision of Dr. MarouaneKessentini in the ISELab. He received my PhDDegree in Computer Science from the Fed-eral University of Parana in 2019. His researchmainly focuses on the use of Preference andSearch Based Software Engineering to addressseveral software engineering problems such asSoftware Testing and Software Refactoring.

Danny Dig is an associate professor of com-puter science at the University of Colorado,and an adjunct professor at University of Illinoisand Oregon State. He successfully pioneeredinteractive program transformations by openingthe field of refactoring in cutting-edge domainsincluding mobile, concurrency and parallelism,component-based, testing, and end-user pro-gramming. He earned his Ph.D. from the Univer-sity of Illinois at Urbana-Champaign where hisresearch won the best Ph.D. dissertation award,

and the First Prize at the ACM Student Research Competition GrandFinals. He did a postdoc at MIT. He (co-)authored 50+ journal andconference papers that appeared in top places in SE/PL. Accordingto Google Scholar his publications have been cited 4000+ times. Hisresearch was recognized with 8 best paper awards at the flagship andtop conferences in SE, 4 award runner-ups, and 1 most influential paperaward (N-10 years) at ICSME’15. He received the NSF CAREER award,the Google Faculty Research Award (twice), and the Microsoft SoftwareEngineering Innovation Award (twice). He released 9 software systems,among them the world’s first open-source refactoring tool. Some of thetechniques he developed are shipping with the official release of thepopular Eclipse, NetBeans, and Visual Studio development environ-ments (of which Eclipse alone had more than 14M downloads in 2014).

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